Incipient faults in power cables are difficult to diagnose because their transient signatures are weak, non-stationary, and easily masked by background noise, while labeled real-world samples are often scarce. To address these challenges, this paper proposes an offline diagnosis framework that integrates Whale Optimization Algorithm (WOA)-guided CEEMDAN with a TCN-BiLSTM-Multi-HeadAttention network. The proposed method has three main features. First, WOA is explicitly mapped to the CEEMDAN parameter optimization problem and is used to adaptively optimize the noise amplitude and ensemble number, thereby improving decomposition quality and enhancing weak fault-related components. Second, the optimized intrinsic mode functions are reconstructed into a multi-channel representation that preserves complementary fault information across different frequency bands. Third, a hybrid deep architecture combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory, and multi-HeadAttention is designed to jointly capture local transient characteristics, bidirectional temporal dependencies, and fault-sensitive feature interactions. Experimental results on both PSCAD/EMTDC simulation data and real-world measured data show that the optimized WOA-CEEMDAN achieves superior decomposition performance, with an RMSE of 0.097 and an SNR of 8.42 dB. On the real-world test dataset, the proposed framework achieves 96.00% accuracy, 97.25% precision, 96.84% recall, an F1-score of 0.970, and an AUC of 0.97, outperforming several representative baseline models. Additional ablation, noise-robustness, small-sample, confusion-matrix, and cross-cable validation results further demonstrate the effectiveness and robustness of the proposed framework for incipient cable fault diagnosis.
Xing et al. (Fri,) studied this question.
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